Matrix Factorization Techniques for Recommender Systems

A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neural network, as well as the machine learning techniques relevant to network anomaly detection. In addition, this article introduces the latest work that employed deep learning techniques with the focus on network anomaly detection through the extensive literature survey. We also discuss our local experiments showing the feasibility of the deep learning approach to network traffic analysis.

A survey of deep learning-based network anomaly detection

Deep learning (DL) has solved a problem that a few years ago was thought to be intractable — the automatic recognition of patterns in spatial and temporal data with an accuracy superior to that of humans. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners who are inundated with all types of data. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge in the field. Where does one start? How does one determine if a particular DL model is applicable to their problem? How does one train and deploy them? With these questions in mind, we present an overview of some of the key DL architectures. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is critical to many applications, a section dwells on using DL for fault detection and mitigation. This is followed by an exploratory survey of several areas where DL emerged as a game-changer: fraud detection in financial applications, financial time-series forecasting, predictive and prescriptive analytics, medical image processing, power systems research and recommender systems. The thrust of this review is to outline emerging applications of DL and provide a reference to researchers seeking to use DL in their work for pattern recognition with unparalleled learning capacity and the ability to scale with data.

A review of deep learning with special emphasis on architectures, applications and recent trends

Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods.

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical methods of RS have achieved remarkable successes in providing item recommendations, they still suffer from many issues such as cold start and data sparsity. With the recent achievements of deep learning in various applications such as Natural Language Processing (NLP) and image processing, more efforts have been made by the researchers to exploit deep learning methods for improving the performance of RS. However, despite the several research works on deep learning based RS, very few secondary studies were conducted in the field. Therefore, this study aims to provide a systematic literature review (SLR) of deep learning based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the field. This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications. The paper particularly adopts an SLR approach based on the standard guidelines of the SLR designed by Kitchemen-ham which uses selection method and provides detail analysis of the research publications. Several publications were gathered and after inclusion/exclusion criteria and the quality assessment, the selected papers were finally used for the review. The results of the review indicated that autoencoder (AE) models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks (CNNs) and the Recurrent Neural Networks (RNNs) models. Also, the results showed that Movie Lenses is the most popularly used datasets for the deep learning-based RS evaluation followed by the Amazon review datasets. Based on the results, the movie and e-commerce have been indicated as the most common domains for RS and that precision and Root Mean Squared Error are the most commonly used metrics for evaluating the performance of the deep leaning based RSs.

Recommendation system based on deep learning methods: a systematic review and new directions

Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey

The Personalization of information has taken recommender systems at a very high level. With personalization these systems can generate user specific recommendations accurately and efficiently. User profiling helps personalization, where information retrieval is done to personalize a scenario which maintains a separate user profile for individual user. The main objective of this paper is to explore this field of personalization in context of user profiling, to help researchers make aware of the user profiling. Various trends, techniques and Applications have been discussed in paper which will fulfill this motto.

User Profiling Trends, Techniques and Applications

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user Collaborative Filtering is currently most widely used approach to build Recommendation System CF techniques uses the user behavior in form of user item ratings as their information source for prediction There are major challenges like sparsity of rating matrix and growing nature of data which is faced by CF algorithms These challenges are been well taken care by Matrix Factorization In this paper we attempt to present an overview on the role of different MF model to address the challenges of CF algorithms, which can be served as a roadmap for research in this area

Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey.

https://isiarticles.com/bundles/Article/pre/pdf/40679.pdf

Introducing Hybrid Technique for Optimization of Book Recommender System

The customers' needs in a software project are identified in the process of Software requirements elicitation. For building a software system this process is considered as one of the most important parts. In this part it is decided precisely what will be built. A close interaction between developers and end-users of the system is needed by requirements’ gathering. Meetings can be costly, inconvenient and infrequent if developers and end-users are in different organizations or different cities. The quality of the elicited requirements can greatly be impacted if there is a problem of communication. Requirement elicitation is a process difficult to scale to large software projects with many stakeholders which involves identifying and prioritizing requirements. A stakeholder is an individual or a group who can influence or be influenced by the success or failure of a project. Existing methods to identify and prioritize requirements do not scale well to large projects. Large projects tend to be beset by three problems: information overload, inadequate stakeholder input, and biased prioritization of requirements. Existing methods to identify and prioritize requirements do not scale well to large projects. Existing requirements prioritization methods require substantial efforts from the requirements engineers when there are many requirements. To address the problems Stakeholder recommender model will contain steps:-Identify the large project, Analysis of requirements, Identify and prioritize stakeholders, Predict requirements, Prioritize requirements. For making predictions, our approach will use one of the most well known algorithms that is k-Nearest Neighbor (kNN) algorithm. KNN is used to identify like-minded users with similar rating histories in order to predict ratings for unobserved users-item pairs. A unique subset of the community for each user is found out by KNN by identifying those with similar interests. To do so, every pair of user profile is compared to measure the degree of similarity. A neighbourhood is created for each user by selecting the k most similar users. The similarity between each pair of user profiles for users in the neighbourhood is used to compute predicted ratings. Finally, the predicted ratings for the items are sorted according to the predicted value, and the top-N items are proposed to the user as recommendations, where N is the number of items recommended to the user.

/pdf/a-new-approach-to-requirement-elicitation-based-on-i39ovcihkj.pdf

Sheetal Girase

Papers

Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey

User Profiling Trends, Techniques and Applications

Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey.

Introducing Hybrid Technique for Optimization of Book Recommender System

A new approach to requirement elicitation based on stakeholder recommendation and collaborative filtering